发表机构
BosonQ Psi Corp; National Quantum Laboratory (QLab), University of Maryland, College Park（BosonQ Psi 公司; 马里兰大学国家量子实验室（QLab））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究在IonQ离子阱硬件上基准测试了最大似然振幅估计QMC管线，发现无噪声下接近海森堡缩放，但有噪声时误差饱和，建议采用浅层调度优化深度。
AI 中文摘要
量子振幅估计有望通过用海森堡极限的ε∝N^{-1}缩放替代经典ε∝N^{-1/2}采样定律来加速蒙特卡洛积分，但在当今硬件上该优势是否有任何部分能够保留是一个经验性问题。我们报告了在IonQ离子阱处理器上对最大似然振幅估计QMC管线的系统性实验基准测试，包括在IonQ Forte-1上进行的60次硬件试验，涵盖五种设置（5和9量子比特，每深度100-200次射击，线性和指数放大调度，约113设备小时），以及在IonQ Aria-1和Forte-1噪声模型上在5、9和19量子比特下进行的100次试验。该管线结合了受控-R_y旋转态制备、基于Grover的振幅放大和最大似然推断，并以b_max^{-1}∫_0^{b_max}sin^2 x dx（其中b_max=π/5）为基准进行测试，结果如下。首先，在无噪声极限下，它达到ε∝N^{-0.88}，接近海森堡缩放参考，在oracle调用增加133倍的情况下将误差降低了54-74倍。其次，这种缩放无法在硬件上保持。每个有噪声的后端都饱和在(2-5)x10^{-2}的误差基底上，Forte-1线性调度的拟合指数为0.04-0.15，而供应商噪声模型的α≤0：然而，超过最优浅深度后，更深的放大无法提供持续的精度提升，反而回到噪声基底。Forte-1硬件在每个深度m≥2时也优于IonQ自己的Aria-1噪声模型，表明供应商噪声模型不是MLAE精度的良好预测器。对UQ的实际意义是，在当前设备上，有用的工作点是浅的、经过调度调整的放大深度，选择该深度使得最深电路接近p~1/2，而不是相干性预算允许的最深调度。
英文摘要
Quantum Amplitude Estimation promises to accelerate Monte Carlo integration by replacing the classical $\varepsilon \propto N^{-1/2}$ sampling law with the Heisenberg-limited $\varepsilon \propto N^{-1}$ scaling, but whether any part of that advantage survives on present-day hardware is an empirical question. We report a systematic experimental benchmark of a Maximum-Likelihood Amplitude Estimation QMC pipeline on IonQ trapped-ion processors, comprising 60 hardware trials on IonQ Forte-1 across five settings(5 & 9 qubits, 100-200 shots per depth, linear & exponential amplification schedules, ~113 device-hours), together with 100 trials on IonQ Aria-1 & Forte-1 noise models at 5, 9 & 19 qubits. The pipeline combines controlled-$R_y$ rotational state preparation, Grover-based amplitude amplification and maximum-likelihood inference and is benchmarked against $b_{\max}^{-1}\!\int_0^{b_{\max}}\!\sin^2\!x\,\mathrm{d}x$ with $b_{\max}=π/5$ ,given results emerge. First, the noiseless limit it attains $\varepsilon \propto N^{-0.88}$, close to the Heisenberg-scaling reference, reducing the error 54-74x over a 133x increase in oracle calls. Second, scaling does not survive on hardware. Every noisy backend saturates at an error floor of (2-5)x 10^{-2}, with fitted exponents of 0.04-0.15 on Forte-1 linear schedules and $α\le\!0$ on the vendor noise models: beyond the optimal shallow depth, however, deeper amplification provides no sustained accuracy gain and instead returns to the noise floor. Forte-1 hardware also outperformed IonQ's own Aria-1 noise model at every depth $m \ge 2$, indicating that vendor noise models are bad predictors of MLAE accuracy. The practical implication for UQ is that on current devices the useful operating point is a shallow, schedule-tuned amplification depth chosen so that the deepest circuit lands near p~1/2 not the deepest schedule the coherence budget allows.
Comments7 pages 3 figures
